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DDIRNet:robust radar emitter recognition via single domain generalization

Journal of Systems Engineering and Electronics 2025年第2期36卷 397-404页

作者： WU Honglin LI Xueqiong HUANG Junjie JIN Ruochun TANG Yuhua Institute for Quantum Information&State Key Laboratory of High Performance Computing National University of Defense TechnologyChangsha 410073China

Automatically recognizing radar emitters from com-plex electromagnetic environments is important but ***,the changing electromagnetic environment results in inconsistent signal distribution in the real world,which makes the existing approaches perform poorly for recognition tasks in different *** this paper,we propose a domain generaliza-tion framework is proposed to improve the adaptability of radar emitter signal recognition in changing ***,we propose an end-to-end denoising based domain-invariant radar emitter recognition network(DDIRNet)consisting of a denoising model and a domain invariant representation learning model(IRLM),which mutually benefit from each *** the signal denoising model,a loss function is proposed to match the feature of the radar signals and guarantee the effectiveness of the *** the domain invariant representation learning model,contrastive learning is introduced to learn the cross-domain feature by aligning the source and unseen domain ***,we design a data augmentation method that improves the diversity of signal data for *** experiments on classification have shown that DDIRNet achieves up to 6.4%improvement compared with the state-of-the-art radar emitter recognition *** proposed method pro-vides a promising direction to solve the radar emitter signal recognition problem.

关键词： radar emitter recognition domain generalization denoising contrastive learning data augmentation.

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Collaborative Pushing and Grasping in Complex Scenarios via a Generator-Evaluator Network with Multiple Action Primitives 17th

Collaborative Pushing and Grasping in Complex Scenarios vi...

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17th International Conference on intelligent Robotics and Applications, ICIRA 2024

作者： Zuo, Guoyu Wang, Zihao Luo, Yongkang Yu, Shuangyue Zhao, Min School of Information Science and Technology Beijing University of Technology Beijing100124 China Beijing Key Laboratory of Computing Intelligence and Intelligent Systems Beijing100124 China The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9789819607761

As robotic technologies advance, robotic arm manipulation tasks in complex environments become increasingly important. This paper presents a new collaborative pushing and grasping strategy to address two major challenges robotic arms encounter in complex environments: invisibility and tight enclosure of target objects. We propose a generator-evaluator network architecture based on multiple action primitives, incorporating hierarchical reinforcement learning and Double DQN techniques to optimize the operational efficiency of the robotic arm. By introducing a new action primitive to prioritize pushing operations around the topmost objects in space, our approach effectively reveals and locates obscured target objects. Additionally, utilizing the Spatial Correlation Test (SCT), our method explores all possible collision-free operational positions in the action space, selecting the optimal position from among them. Experimental results demonstrate that our approach achieves high efficiency and success rates in scenarios with randomly distributed objects, objects tightly enclosed by others, and invisible objects. This strategy enhances the speed and accuracy of tasks and improves the robot’s adaptability to complex environments. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Robotic arms

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Multi-Scale Time Series Segmentation Network Based on Eddy Current Testing for Detecting Surface Metal Defects

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 528-538页

作者： Xiaorui Li Xiaojuan Ban Haoran Qiao Zhaolin Yuan Hong-Ning Dai Chao Yao Yu Guo Mohammad S.Obaidat George Q.Huang the School of Intelligence Science and Technology University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering the Key Laboratory of Intelligent Bionic Unmanned Systems and the Institute of Materials Intelligent Technology Liaoning Academy of Materials IEEE the Department of Computer Science Hong Kong Baptist University the School of Computer and Communication Engineering Key Laboratory of Advanced Materials and Devices for Post-Moore Chips Ministry of Education University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology Beijing the School of Computer and Communication Engineering University of Science and Technology Beijing the King Abdullah Ⅱ School of Information Technology The University of Jordan the Department of Computational Intelligence the School of Computing SRM University the School of Engineering The Amity University The Hong Kong Polytechnic University

In high-risk industrial environments like nuclear power plants, precise defect identification and localization are essential for maintaining production stability and safety. However, the complexity of such a harsh environment leads to significant variations in the shape and size of the defects. To address this challenge, we propose the multivariate time series segmentation network(MSSN), which adopts a multiscale convolutional network with multi-stage and depth-separable convolutions for efficient feature extraction through variable-length templates. To tackle the classification difficulty caused by structural signal variance, MSSN employs logarithmic normalization to adjust instance distributions. Furthermore, it integrates classification with smoothing loss functions to accurately identify defect segments amid similar structural and defect signal subsequences. Our algorithm evaluated on both the Mackey-Glass dataset and industrial dataset achieves over 95% localization and demonstrates the capture capability on the synthetic dataset. In a nuclear plant's heat transfer tube dataset, it captures 90% of defect instances with75% middle localization F1 score.

关键词： Eddy current testing nondestructive testing semantic segmentation time series analysis

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CONVERGENCE ANALYSIS OF SOME FINITE ELEMENT PARALLEL ALGORITHMS FOR THE STATIONARY INCOMPRESSIBLE MHD EQUATIONS

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Journal of Computational Mathematics 2024年第1期42卷 49-70页

作者： Xiaojing Dong Yinnian He Hunan Key Laboratory for Computation and Simulation in Science and Engineering Key Laboratory of Intelligent Computing&Information Processing of Ministry of EducationSchool of Mathematics and Computational ScienceXiangtan UniversityXiangtan 411105China Institute of Applied Physics and Computational Mathematics Beijing 100088China School of Mathematics and Statistics Xi'an Jiaotong UniversityXi'an 710049China

By combination of iteration methods with the partition of unity method(PUM),some finite element parallel algorithms for the stationary incompressible magnetohydrodynamics(MHD)with different physical parameters are presented and *** algorithms are highly *** first,a global solution is obtained on a coarse grid for all approaches by one of the iteration *** parallelized residual schemes,local corrected solutions are calculated on finer meshes with overlapping *** subdomains can be achieved flexibly by a class of *** proposed algorithm is proved to be uniformly stable and ***,one numerical example is presented to confirm the theoretical findings.

关键词： Partition of unity method Local and parallel algorithm Finite element method Iteration methods Magnetohydrodynamics

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Uncertainty-aware Superpoint Graph Transformer for Weakly Supervised 3D Semantic Segmentation

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IEEE Transactions on Fuzzy Systems 2025年第6期33卷 1899-1912页

作者： Fan, Yan Wang, Yu Zhu, Pengfei Hui, Le Xie, Jin Hu, Qinghua Tianjin University College of Intelligence and Computing Tianjin300350 China Ministry of Education of the People's Republic of China Engineering Research Center of City Intelligence and Digital Governance China Haihe Laboratory of Itai Tianjin China Northwestern Polytechnical University School of Electronics and Information Shaanxi Key Laboratory of Information Acquisition and Processing China Nanjing University of Science and Technology Pca Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education China Nanjing University State Key Laboratory for Novel Software Technology Nanjing China

Weakly supervised 3D semantic segmentation has successfully mitigated the labor-intensive and time-consuming task of annotating 3D point clouds. However, reliably utilizing the minimal point-wise annotations for unlabeled data in complex and large-scale scenes is still challenging, such as only 20 points labeled in 2 million points. To tackle this challenge, we propose a new Uncertainty-aware Superpoint Graph Transformer (UaSGT) framework that utilizes minimal annotations for unlabeled data learning through reliable long-range supervision propagation from labeled superpoints to unlabeled superpoints. First, we propose a superpoint graph transformer to achieve long-range supervision propagation along the attention-based fuzzy subsets defined on superpoints. The attention-based fuzzy subset measures the membership of unlabeled superpoints to clusters centered on labeled superpoints. Second, we employ an uncertainty-aware membership rectification technique on the fuzzy subset to ensure reliable propagation among superpoints within the same category. This technique integrates an uncertainty prediction module to mask the influence of unreliable membership and a spatial prior refinement module to reduce uncertainty in intra-class membership degrees. Finally, experimental results on two large-scale benchmarks S3DIS and ScanNet-V2 demonstrate the superiority of our approach compared to the state-of-the-art with at least 90% annotation reduction, and our method also achieves comparable performance to fully supervised methods with less than 0.1% labeled points. Our code is available at https://***/fanyan0411/UaSGT. © 2025 IEEE.

关键词： Semantic Segmentation

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Spatial-temporal distribution and emission of urban scale air pollutants in Hefei based on Mobile-DOAS

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Journal of Environmental Sciences 2025年第5期151卷 238-251页

作者： Zhidong Zhang Pinhua Xie Ang Li Min Qin Jin Xu Zhaokun Hu Xin Tian Feng Hu Yinsheng Lv Jiangyi Zheng Youtao Li School of Environmental Science and Optoelectronic Technology University of Science and Technology of ChinaHefei 230026China Key Laboratory of Environmental Optics and Technology Anhui Institute of Optics and Fine MechanicsHefei Institutes of Physical ScienceChinese Academy of SciencesHefei 230031China University of Chinese Academy of Sciences Beijing 100049China Information Materials and Intelligent Sensing Laboratory of Anhui Province Institutes of Physical Science and Information TechnologyAnhui UniversityHefei 230601China

As a significant city in the Yangtze River Delta regions,Hefei has experienced rapid changes in the sources of air pollution due to its high-speed economic development and urban ***,there has been limited research in recent years on the spatial-temporal distribution and emission of its atmospheric *** address this,this study conducted mobile observations of urban roads using the Mobile-DOAS instrument from June 2021 to May *** monitoring results exhibit a favourable consistent with TROPOMI satellite data and ground monitoring station ***,there were pronounced seasonal variations in air ***,high concentration of HCHO and NO_(2)were closely associated with traffic congestion on roadways,while heightened SO_(2)levels were attributed to winter heating and industrial *** study also revealed that with the implementation of road policies,the average vehicle speed increased by 95.4%,while the NO concentration decreased by 54.4%.In the estimation of urban NO_(x)emission flux,it was observed that in temporal terms,compared with inventory data,the emissions calculated viamobile measurements exhibitedmore distinct seasonal patterns,with the highest emission rate of 349 g/sec in winter and the lowest of 142 g/sec in *** spatial terms,the significant difference in emissions between the inner and outer ring roads also suggests the presence of the city’s primary NO_(x)emission sources in the area between these two *** study offers data support for formulating the next phase of air pollution control measures in urban areas.

关键词： Mobile-DOAS HCHO NO_(2) SO_(2) Spatial-temporal distribution NOx emission

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Multiagent trajectory prediction with global-local scene-enhanced social interaction graph network

Multiagent trajectory prediction with global-local scene-enh...

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作者： Lin, Xuanqi Zhang, Yong Wang, Shun Piao, Xinglin Yin, Baocai Beijing Key Laboratory of Multimedia and Intelligent Software Technology Beijing Artificial Intelligence Institute Faculty of Information Technology Beijing University of Technology Beijing China

Trajectory prediction is essential for intelligent autonomous systems like autonomous driving, behavior analysis, and service robotics. Deep learning has emerged as the predominant technique due to its superior modeling capability for trajectory data. However, deep learning-based models face challenges in effectively utilizing scene information and accurately modeling agent interactions, largely due to the complexity and uncertainty of real-world scenarios. To mitigate these challenges, this study presents a novel multiagent trajectory prediction model, termed the global-local scene-enhanced social interaction graph network (GLSESIGN), which incorporates two pivotal strategies: global-local scene information utilization and a social adaptive attention graph network. The model hierarchically learns scene information relevant to multiple intelligent agents, thereby enhancing the understanding of complex scenes. Additionally, it adaptively captures social interactions, improving adaptability to diverse interaction patterns through sparse graph structures. This model not only improves the understanding of complex scenes but also accurately predicts future trajectories of multiple intelligent agents by flexibly modeling intricate interactions. Experimental validation on public datasets substantiates the efficacy of the proposed model. This research offers a novel model to address the complexity and uncertainty in multiagent trajectory prediction, providing more accurate predictive support in practical application scenarios. © 2024 John Wiley & Sons Ltd.

关键词： Multi agent systems

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On the Probability of Generating a Primitive Matrix

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Journal of Systems Science & Complexity 2024年第4期37卷 1755-1771页

作者： CHEN Jingwei FENG Yong LIU Yang WU Wenyuan Chongqing Key Laboratory of Automated Reasoning and Cognition Chongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqing 400714China Chongqing College University of Chinese Academy of SciencesChongqing 400714China Key Laboratory of Advanced Manufacturing Technology of Ministry of Education Guizhou UniversityyGuiyang 550025China Information Science and Engineering Chongqing Jiaotong UniversityChongqing 400074China

Given a k×n integer primitive matrix A(i.e.,a matrix can be extended to an n×n unimodular matrix over the integers)with the maximal absolute value of entries‖A‖bounded by an integer λ from above,the authors study the probability that the m×n matrix extended from A by appending other m-k row vectors of dimension n with entries chosen randomly and independently from the uniform distribution over{0,1,…,λ-1}is still *** authors present a complete and rigorous proof of a lower bound on the probability,which is at least a constant for fixed m in the range[k+1,n-4].As an application,the authors prove that there exists a fast Las Vegas algorithm that completes a k×n primitive matrix A to an n×n unimodular matrix within expected O(n^(ω)log‖A‖)bit operations,where O is big-O but without log factors,ω is the exponent on the arithmetic operations of matrix multiplication.

关键词： Integer matrix matrix completion probabilistic algorithm unimodular matrix

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Polynomial stacked-attention network for nationality classification

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Frontiers of Computer Science 2022年第4期16卷 215-217页

作者： Kunyan Li Jie Zhang Shiguang Shan Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences(CAS) Institute of Computing TechnologyCASBeijing100190China University of Chinese Academy of Sciences Beijing100049China

1Introduction In recent years,nationality and region inference has atracted more and_more *** anthropological researches indicate that human faces are influenced by genes,and other external factors,e.g-,geographical environment[1].This relevance provides possible solutions for geographical area inference by ***,it remains a challenging task due to the tiny differences on faces for various countries,which is even hard for human beings to tell.

关键词： Polynomial s. network

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Extinction Chains Reveal Intermediate Phases Between the Safety and Collapse in Mutualistic Ecosystems

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Engineering 2024年第12期43卷 89-98页

作者： Guangwei Wang Xueming Liu Ying Xiao Ye Yuan Linqiang Pan Xiaohong Guan Jianxi Gao Hai-Tao Zhang The MOE Engineering Research Center of Autonomous Intelligent Unmanned Systems State Key Laboratory of Digital Manufacturing Equipment and TechnologySchool of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China Guangdong HUST Industrial Technology Research Institute Huazhong University of Science and TechnologyDongguan 523808China Guangdong Provincial Engineering Technology Center of Autonomous Unmanned Vessels Dongguan 523808China MOE Key Laboratory of Intelligent Networks and Network Security&State Key Laboratory of Manufacturing Systems Xi’an Jiaotong UniversityXi’an 710049China Tsinghua National Laboratory of Information Science and Technology Department of AutomationTsinghua UniversityBeijing 100084China Department of Computer Science Rensselaer Polytechnic InstituteTroyNY 12180USA

Ecosystems are undergoing unprecedented persistent deterioration due to unsustainable anthropogenic human activities,such as overfishing and deforestation,and the effects of such damage on ecological stability are *** recent advances in experimental and theoretical studies on regime shifts and tipping points,theoretical tools for understanding the extinction chain,which is the sequence of species extinctions resulting from overexploitation,are still lacking,especially for large-scale nonlinear networked *** this study,we developed a mathematical tool to predict regime shifts and extinction chains in ecosystems under multiple exploitation situations and verified it in 26 real-world mutualistic networks of various sizes and *** discovered five phases during the exploitation process:safe,partial extinction,bistable,tristable,and collapse,which enabled the optimal design of restoration strategies for degraded or collapsed *** validated our approach using a 20-year dataset from an eelgrass restoration ***,we also found a specific region in the diagram spanning exploitation rates and competition intensities,where exploiting more species helps increase *** computational tool provides insights into harvesting,fishing,exploitation,or deforestation plans while conserving or restoring the biodiversity of mutualistic ecosystems.

关键词： Complex system Network science Overexploitation Regime shift Metastability

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